Vision-based multi-scaled vehicle detection and distance relevant mix tracking for driver assistance system

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• 作者：Qin Gu ; Jianyu Yang ; Yuqiang Zhai ; Lingjiang Kong
• 关键词：Driver assistance system ; License detection ; Vehicle detection ; Vehicle tracking ; Inverse perspective mapping
• 刊名：Optical Review
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：22
• 期：2
• 页码：197-209
• 全文大小：2,924 KB
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• 作者单位：Qin Gu (1)
  Jianyu Yang (1)
  Yuqiang Zhai (1)
  Lingjiang Kong (1)
  
  1. University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West Hi-tech Zone, Chengdu, 611731, China
  
• 刊物类别：Physics and Astronomy
• 刊物主题：Physics
  Electromagnetism, Optics and Lasers
  
• 出版者：The Optical Society of Japan, co-published with Springer-Verlag GmbH
• ISSN：1349-9432

文摘

This paper aims to improve the robustness of vision-based multi-scaled vehicle detection and tracking for an actual driver assistance system. Considering the problem of discontinuity of detection and tracking for multi-scaled vehicles especially in an ultra-close area, we propose a novel detection framework which concludes short-range local feature (license plate) detection and long-range skeleton detection. Specially, the rear license plate can be located accurately by introducing a multi-scaled morphological operator and analyzing the color information. Then, vehicles in a long supervising range can be detected with a Look-up Table-based AdaBoost classifier synchronically. Finally, an inverse perspective mapping-based tracking strategy is proposed to unite the location results in the framework. It is proved to make up the leak vehicle detection in the near supervising area and improve the robustness of tracking. The accuracy of license-based detection and the robust mix tracking have both been testified in several groups of experiments.